TeaTime4schools: Joint analysis - fungi
06 Alpha diversity analysis
Roey Angel
2021-05-21
Alpha diversity analysis
This analysis explores the alpha-diversity distribution patters in the different samples, based on the DADA2-produced sequences.
Setting general parameters:
set.seed(1000)
subsamples <- 1000
min_lib_size <- 5000
metadata_path <- "./"
data_path <- "./DADA2_pseudo/"
Ps_file <- "TeaTime4Schools_ITS_filt.RDS"
Proj_name <- "TeaTime4Schools"Load phyloseq object
This phyloseq object was created in 05_Taxonomical_analysis.html. The Ps_obj_filt object excludes contaminants and all sequences classified as eukaryota, chloroplast, mitochondria or unknown but still includes taxa with low prevalence
Ps_obj_filt <- readRDS(file = paste0(data_path, Ps_file))
Ps_obj_filt %>%
subset_samples(., sample_sums(Ps_obj_filt) > min_lib_size) %>% # drop samples below min_lib_size
subset_samples(., Field != "Unburied") %>% # drop unburied samples
filter_taxa(., function(x)
sum(x) > 0, TRUE) -> # remove taxa with 0 abundance
Ps_obj_filt_subset
Ps_obj_filt_subset %>%
get_variable() %>%
mutate_at(., "Sample.type",
~fct_relevel(., levels = c("Soil", "Green tea", "Rooibos"))) %>%
mutate_at(., "Season",
~fct_relevel(., levels = c("Winter", "Spring", "Summer", "Autumn"))) %>%
arrange(Field, Sample.type, Season, Replicate) %>%
pull(Description) %>%
as.character() ->
Sample.orderRichness
# tic()
abundance_mat <- as(otu_table(Ps_obj_filt_subset), "matrix") # use Ps_obj_filt_subset - no contaminants, no euk, chloro, mito, unknowns
## Original data
Original <-
data.frame(Reads = rowSums(abundance_mat),
S = apply(abundance_mat, 1, function(x)
sum(x > 0)))
# Assign rarefaction mat
rarefaction_mat <-
matrix(0, nrow = nrow(abundance_mat), ncol = subsamples)
rownames(rarefaction_mat) <- rownames(abundance_mat)
# Declare richness est table
rich.ests <-
list(
S.obs = rarefaction_mat,
S.chao1 = rarefaction_mat,
se.chao1 = rarefaction_mat,
S.ACE = rarefaction_mat,
se.ACE = rarefaction_mat
)
# Rarefy abundance_mat and calc estimates
for (i in seq(subsamples)) {
sub.OTUmat <-
rrarefy(abundance_mat, min(rowSums(abundance_mat)))
# rrarefy(abundance_mat, quantile(rowSums(abundance_mat), probs = seq(0, 1, rare2quant))[2])
for (j in seq(length(rich.ests))) {
rich.ests[[j]][, i] <- t(estimateR(sub.OTUmat))[, j]
}
}
# Calculate means and SEM of subsamples
Richness <- data.frame(row.names = row.names(rich.ests[[1]]))
for (i in c(1, seq(2, length(rich.ests), 2))) {
S <- apply(rich.ests[[i]], 1, mean)
if (i == 1) {
se <-
apply(rich.ests[[i]], 1, function(x)
(mean(x) / sqrt(length(x))))
} else
se <- apply(rich.ests[[i + 1]], 1, mean)
Richness <- cbind(Richness, S, se)
}
colnames(Richness) <-
c("S.obs.Estimate",
"S.obs.SE",
"S.chao1.Estimate",
"S.chao1.SE",
"S.ACE.Estimate",
"S.ACE.SE")Merge tables and save richness results
# Richness.Table <- cbind(Richness, parametric)
# Richness.Table <- Richness # only
saveRDS(cbind(Original, Richness),
file = paste0(Proj_name, "_Richness.RDS"))
write.csv(cbind(Original, Richness),
file = paste0(Proj_name, "_Richness.csv"))Plot richness estimates
Richness %>%
rownames_to_column(var = "Sample") %>% # get sample names
mutate_at(., "Sample", ~str_replace_all(., "Green_tea", "Green tea")) %>% #
mutate_at(., "Sample", ~fct_relevel(., levels = Sample.order)) %>%
bind_cols(., get_variable(Ps_obj_filt_subset,
c("Lib.size", "Field", "Sample.type", "Season", "Replicate"))) %>% # add metadata
pivot_longer(cols = c(-"Sample", -"Lib.size", -"Field", -"Sample.type", -"Season", -"Replicate"),
names_to = c("Metric", ".value"),
values_to = c("Estimate", "SE"),
names_pattern = "(.*\\..*)\\.(.*)") %>% # gather all metrices to one column
mutate_at(., "Season",
~fct_relevel(., levels = c("Winter", "Spring", "Summer", "Autumn"))) %>%
mutate_at(., "Metric", ~fct_recode(., `Observed S` = "S.obs", Chao1 = "S.chao1", ACE = "S.ACE")) %>%
mutate(Estimate, herr = Estimate + SE, lerr = Estimate - SE) ->
Richness_long
PlotSummarySingle_overlay(Richness_long)
fit <- lm(Estimate ~ Lib.size,
data = Richness_long[Richness_long$Metric == "Observed S", ])
fit$model$Sample <- str_replace(Richness_long[Richness_long$Metric == "Observed S", ]$Sample, "(.*)_[0-9]$", "\\1")
ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) +
geom_point(aes(colour = fit$model$Sample)) +
stat_smooth(method = "lm", col = "red") +
labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
"; Intercept =",signif(fit$coef[[1]], 5),
"; Slope =",signif(fit$coef[[2]], 5),
"; P =",signif(summary(fit)$coef[2,4], 5))) +
theme(plot.title = element_text(size = 8)) +
theme(legend.position = "none")
PlotSummarySingle_overlay(x = "Season", Richness_long) +
facet_grid(Field ~ Sample.type) 
Combine replicates
Richness_long %>%
group_by(Field, Season, Sample.type, Metric) %>%
summarise(
Mean = mean(Estimate),
lerr = Mean - (sd(Estimate)) / sqrt(length(Estimate)),
herr = Mean + (sd(Estimate)) / sqrt(length(Estimate))
) ->
Combined_Richness_long
# plot
PlotSummarySingle_overlay(x = "Season", y = "Mean", Combined_Richness_long) +
facet_grid(Field ~ Sample.type) 
Diversity
# calculate diversity indices
# declare diversity indices table
diversity.inds <-
list(Shannon = rarefaction_mat,
inv.simpson = rarefaction_mat,
BP = rarefaction_mat)
# rarefy abundance_mat and calc estimates
for (i in seq(subsamples)) {
sub.OTUmat <-
rrarefy(abundance_mat, min(rowSums(abundance_mat)))
# rrarefy(abundance_mat, quantile(rowSums(abundance_mat), probs = seq(0, 1, rare2quant))[2])
diversity.inds$Shannon[, i] <-
diversityresult(sub.OTUmat,
index = 'Shannon' ,
method = 'each site',
digits = 3)[, 1]
diversity.inds$inv.simpson[, i] <-
diversityresult(sub.OTUmat,
index = 'inverseSimpson' ,
method = 'each site',
digits = 3)[, 1]
diversity.inds$BP[, i] <-
diversityresult(sub.OTUmat,
index = 'Berger' ,
method = 'each site',
digits = 3)[, 1]
}
# calculate means and SEM of subsamples
Diversity <-
data.frame(row.names = row.names(diversity.inds[[1]]))
for (i in seq(length(diversity.inds))) {
S <- apply(diversity.inds[[i]], 1, mean)
se <-
apply(diversity.inds[[i]], 1, function(x)
(mean(x) / sqrt(length(x))))
Diversity <- cbind(Diversity, S, se)
}
colnames(Diversity) <-
c("Shannon.Estimate",
"Shannon.SE",
"InvSimpson.Estimate",
"InvSimpson.SE",
"BP.Estimate",
"BP.SE")Save diversity results
saveRDS(Diversity, file = paste0(Proj_name, "_Diversity.RDS"))
write.csv(Diversity, file = paste0(Proj_name, "_Diversity.csv"))Plot diversity indices
Diversity %>%
rownames_to_column(var = "Sample") %>% # get sample names
mutate_at(., "Sample", ~str_replace_all(., "Green_tea", "Green tea")) %>% #
mutate_at(., "Sample", ~fct_relevel(., levels = Sample.order)) %>%
bind_cols(., get_variable(Ps_obj_filt_subset,
c("Lib.size", "Field", "Sample.type", "Season", "Replicate"))) %>% # add metadata
pivot_longer(cols = c(-"Sample", -"Lib.size", -"Field", -"Sample.type", -"Season", -"Replicate"),
names_to = c("Metric", ".value"),
values_to = c("Estimate", "SE"),
names_pattern = "(.*)\\.(.*)") %>% # gather all metrices to one column
mutate_at(., "Season",
~fct_relevel(., levels = c("Winter", "Spring", "Summer", "Autumn"))) %>%
mutate_at(., "Metric", ~fct_recode(., "Inv. Simpson" = "InvSimpson", "Berger Parker" = "BP")) %>%
mutate(Estimate, herr = Estimate + SE, lerr = Estimate - SE) ->
Diversity_long
PlotSummarySingle(Diversity_long, y = "Estimate", colour = "Metric") +
background_grid(major = "y", minor = "none", size.major = 0.8) 
PlotSummarySingle(Diversity_long, x = "Season", y = "Estimate", colour = "Metric") +
facet_grid(Metric ~ Sample.type, scale = "free") +
background_grid(major = "y", minor = "none", size.major = 0.8)
Combine replicates
Diversity_long %>%
group_by(Field, Season, Sample.type, Metric) %>%
summarise(
Mean = mean(Estimate),
lerr = Mean - (sd(Estimate)) / sqrt(length(Estimate)),
herr = Mean + (sd(Estimate)) / sqrt(length(Estimate))
) ->
Combined_Diversity_long
# plot
p <- PlotSummarySingle(Combined_Diversity_long, x = "Sample.type", y = "Mean", colour = "Metric")
p$layers[[1]] <- NULL
p +
geom_point(aes_string(colour = "Metric", shape = "Season"), size = 3) +
facet_grid(Metric ~ Field, scales = "free") +
background_grid(major = "y", minor = "none", size.major = 0.8) 
(mod_obsS <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Observed S")))## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 826668 58912 9484 132159
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 34.8
## Estimated effects may be unbalanced
## [1] "Unequal group sizes - showing SS type III"
## Anova Table (Type III tests)
##
## Response: Estimate
## Sum Sq Df F value Pr(>F)
## (Intercept) 60231 1 49.68 1.7e-10 ***
## Sample.type 281178 2 115.95 < 2e-16 ***
## Season 12837 3 3.53 0.017 *
## Sample.type:Season 9484 6 1.30 0.262
## Residuals 132159 109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tables of means
## Grand mean
##
## 132
##
## Sample.type
## Green tea Rooibos Soil
## 73.5 95.2 275
## rep 45.0 46.0 30
##
## Season
## Autumn Spring Summer Winter
## 137 114 109 165
## rep 33 28 30 30
##
## Sample.type:Season
## Season
## Sample.type Autumn Spring Summer Winter
## Green tea 71 61 58 100
## rep 12 11 10 12
## Rooibos 100 75 79 124
## rep 12 11 11 12
## Soil 291 253 236 331
## rep 9 6 9 6## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 826668 58912 9484 132159
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 34.8
## Estimated effects may be unbalanced# Post-hoc test
marginal <- emmeans(mod_obsS,
~ Sample.type : Season)
summary(marginal)## Sample.type Season emmean SE df lower.CL upper.CL
## Green tea Autumn 71 10.1 109 50.9 91
## Rooibos Autumn 100 10.1 109 79.6 119
## Soil Autumn 291 11.6 109 267.6 314
## Green tea Spring 61 10.5 109 40.1 82
## Rooibos Spring 75 10.5 109 54.4 96
## Soil Spring 253 14.2 109 224.6 281
## Green tea Summer 58 11.0 109 36.3 80
## Rooibos Summer 79 10.5 109 58.3 100
## Soil Summer 236 11.6 109 212.5 259
## Green tea Winter 100 10.1 109 80.4 120
## Rooibos Winter 124 10.1 109 104.1 144
## Soil Winter 331 14.2 109 303.0 359
##
## Confidence level used: 0.95contrast(marginal,
method = "pairwise",
adjust = "tukey")## contrast estimate SE df t.ratio p.value
## Green tea Autumn - Rooibos Autumn -28.7 14.2 109 -2.020 0.6790
## Green tea Autumn - Soil Autumn -219.8 15.3 109 -14.310 <.0001
## Green tea Autumn - Green tea Spring 9.9 14.5 109 0.680 1.0000
## Green tea Autumn - Rooibos Spring -4.4 14.5 109 -0.300 1.0000
## Green tea Autumn - Soil Spring -181.9 17.4 109 -10.450 <.0001
## Green tea Autumn - Green tea Summer 12.7 14.9 109 0.850 0.9990
## Green tea Autumn - Rooibos Summer -8.3 14.5 109 -0.570 1.0000
## Green tea Autumn - Soil Summer -164.7 15.3 109 -10.730 <.0001
## Green tea Autumn - Green tea Winter -29.5 14.2 109 -2.080 0.6400
## Green tea Autumn - Rooibos Winter -53.2 14.2 109 -3.740 0.0150
## Green tea Autumn - Soil Winter -260.3 17.4 109 -14.950 <.0001
## Rooibos Autumn - Soil Autumn -191.1 15.3 109 -12.440 <.0001
## Rooibos Autumn - Green tea Spring 38.6 14.5 109 2.660 0.2630
## Rooibos Autumn - Rooibos Spring 24.3 14.5 109 1.670 0.8760
## Rooibos Autumn - Soil Spring -153.2 17.4 109 -8.800 <.0001
## Rooibos Autumn - Green tea Summer 41.4 14.9 109 2.780 0.2040
## Rooibos Autumn - Rooibos Summer 20.4 14.5 109 1.410 0.9600
## Rooibos Autumn - Soil Summer -136.0 15.3 109 -8.860 <.0001
## Rooibos Autumn - Green tea Winter -0.8 14.2 109 -0.060 1.0000
## Rooibos Autumn - Rooibos Winter -24.5 14.2 109 -1.720 0.8550
## Rooibos Autumn - Soil Winter -231.6 17.4 109 -13.300 <.0001
## Soil Autumn - Green tea Spring 229.7 15.7 109 14.680 <.0001
## Soil Autumn - Rooibos Spring 215.4 15.7 109 13.760 <.0001
## Soil Autumn - Soil Spring 37.9 18.4 109 2.060 0.6480
## Soil Autumn - Green tea Summer 232.5 16.0 109 14.530 <.0001
## Soil Autumn - Rooibos Summer 211.5 15.7 109 13.510 <.0001
## Soil Autumn - Soil Summer 55.1 16.4 109 3.360 0.0480
## Soil Autumn - Green tea Winter 190.3 15.3 109 12.390 <.0001
## Soil Autumn - Rooibos Winter 166.6 15.3 109 10.850 <.0001
## Soil Autumn - Soil Winter -40.6 18.4 109 -2.210 0.5460
## Green tea Spring - Rooibos Spring -14.3 14.8 109 -0.960 0.9980
## Green tea Spring - Soil Spring -191.8 17.7 109 -10.850 <.0001
## Green tea Spring - Green tea Summer 2.8 15.2 109 0.190 1.0000
## Green tea Spring - Rooibos Summer -18.2 14.8 109 -1.220 0.9860
## Green tea Spring - Soil Summer -174.6 15.7 109 -11.150 <.0001
## Green tea Spring - Green tea Winter -39.4 14.5 109 -2.710 0.2350
## Green tea Spring - Rooibos Winter -63.1 14.5 109 -4.340 0.0020
## Green tea Spring - Soil Winter -270.2 17.7 109 -15.290 <.0001
## Rooibos Spring - Soil Spring -177.5 17.7 109 -10.050 <.0001
## Rooibos Spring - Green tea Summer 17.1 15.2 109 1.120 0.9930
## Rooibos Spring - Rooibos Summer -3.9 14.8 109 -0.260 1.0000
## Rooibos Spring - Soil Summer -160.3 15.7 109 -10.240 <.0001
## Rooibos Spring - Green tea Winter -25.1 14.5 109 -1.730 0.8500
## Rooibos Spring - Rooibos Winter -48.8 14.5 109 -3.360 0.0480
## Rooibos Spring - Soil Winter -256.0 17.7 109 -14.480 <.0001
## Soil Spring - Green tea Summer 194.6 18.0 109 10.820 <.0001
## Soil Spring - Rooibos Summer 173.6 17.7 109 9.820 <.0001
## Soil Spring - Soil Summer 17.2 18.4 109 0.940 0.9990
## Soil Spring - Green tea Winter 152.4 17.4 109 8.750 <.0001
## Soil Spring - Rooibos Winter 128.7 17.4 109 7.390 <.0001
## Soil Spring - Soil Winter -78.4 20.1 109 -3.900 0.0090
## Green tea Summer - Rooibos Summer -21.0 15.2 109 -1.380 0.9650
## Green tea Summer - Soil Summer -177.4 16.0 109 -11.090 <.0001
## Green tea Summer - Green tea Winter -42.2 14.9 109 -2.830 0.1810
## Green tea Summer - Rooibos Winter -65.9 14.9 109 -4.420 0.0010
## Green tea Summer - Soil Winter -273.1 18.0 109 -15.190 <.0001
## Rooibos Summer - Soil Summer -156.4 15.7 109 -9.990 <.0001
## Rooibos Summer - Green tea Winter -21.2 14.5 109 -1.460 0.9480
## Rooibos Summer - Rooibos Winter -44.9 14.5 109 -3.090 0.0980
## Rooibos Summer - Soil Winter -252.1 17.7 109 -14.260 <.0001
## Soil Summer - Green tea Winter 135.2 15.3 109 8.800 <.0001
## Soil Summer - Rooibos Winter 111.5 15.3 109 7.260 <.0001
## Soil Summer - Soil Winter -95.7 18.4 109 -5.210 <.0001
## Green tea Winter - Rooibos Winter -23.7 14.2 109 -1.660 0.8800
## Green tea Winter - Soil Winter -230.8 17.4 109 -13.260 <.0001
## Rooibos Winter - Soil Winter -207.2 17.4 109 -11.900 <.0001
##
## P value adjustment: tukey method for comparing a family of 12 estimates(obsS_pairwise <- cld(marginal,
alpha = 0.05,
Letters = letters,
adjust = "tukey")) # works with lm but not with two-factor ART## Sample.type Season emmean SE df lower.CL upper.CL .group
## Green tea Summer 58 11.0 109 26.0 90 a
## Green tea Spring 61 10.5 109 30.3 92 a
## Green tea Autumn 71 10.1 109 41.5 100 a
## Rooibos Spring 75 10.5 109 44.6 106 a
## Rooibos Summer 79 10.5 109 48.5 110 ab
## Rooibos Autumn 100 10.1 109 70.2 129 ab
## Green tea Winter 100 10.1 109 71.0 130 ab
## Rooibos Winter 124 10.1 109 94.7 153 b
## Soil Summer 236 11.6 109 201.7 269 c
## Soil Spring 253 14.2 109 211.2 294 cd
## Soil Autumn 291 11.6 109 256.8 325 de
## Soil Winter 331 14.2 109 289.7 373 e
##
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 12 estimates
## P value adjustment: tukey method for comparing a family of 12 estimates
## significance level used: alpha = 0.05(mod_obsS %>%
anova() %>%
mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
mod_obsS_ANOVA)## Analysis of Variance Table
##
## Response: Estimate
## Df Sum Sq Mean Sq F value Pr(>F) Part Eta Sq
## Sample.type 2 826668 413334 340.9 0.000 0.805
## Season 3 58912 19637 16.2 0.000 0.057
## Sample.type:Season 6 9484 1581 1.3 0.262 0.009
## Residuals 109 132159 1212 0.129# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_obsS, Sample.type ~ Season)
# summary(as.glht(pairs(marginal))) # fails because of unbalanced design
(mod_S <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Shannon")))## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 59.3 2.2 0.9 28.9
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 0.515
## Estimated effects may be unbalanced
## [1] "Unequal group sizes - showing SS type III"
## Anova Table (Type III tests)
##
## Response: Estimate
## Sum Sq Df F value Pr(>F)
## (Intercept) 1028 1 3874.26 <2e-16 ***
## Sample.type 58 2 108.95 <2e-16 ***
## Season 2 3 2.48 0.065 .
## Sample.type:Season 1 6 0.55 0.766
## Residuals 29 109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tables of means
## Grand mean
##
## 2.86
##
## Sample.type
## Green tea Rooibos Soil
## 2.31 2.62 4.06
## rep 45.00 46.00 30.00
##
## Season
## Autumn Spring Summer Winter
## 2.76 2.7 3.01 2.99
## rep 33.00 28.0 30.00 30.00
##
## Sample.type:Season
## Season
## Sample.type Autumn Spring Summer Winter
## Green tea 2.34 2.16 2.40 2.35
## rep 12.00 11.00 10.00 12.00
## Rooibos 2.39 2.40 2.88 2.82
## rep 12.00 11.00 11.00 12.00
## Soil 3.95 3.99 4.13 4.21
## rep 9.00 6.00 9.00 6.00## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 59.3 2.2 0.9 28.9
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 0.515
## Estimated effects may be unbalanced# Post-hoc test
marginal <- emmeans(mod_S,
~ Sample.type : Season)
summary(marginal)## Sample.type Season emmean SE df lower.CL upper.CL
## Green tea Autumn 2.34 0.149 109 2.04 2.63
## Rooibos Autumn 2.39 0.149 109 2.09 2.68
## Soil Autumn 3.95 0.172 109 3.61 4.29
## Green tea Spring 2.16 0.155 109 1.86 2.47
## Rooibos Spring 2.40 0.155 109 2.09 2.71
## Soil Spring 3.99 0.210 109 3.57 4.40
## Green tea Summer 2.40 0.163 109 2.08 2.73
## Rooibos Summer 2.88 0.155 109 2.58 3.19
## Soil Summer 4.13 0.172 109 3.79 4.47
## Green tea Winter 2.35 0.149 109 2.06 2.65
## Rooibos Winter 2.82 0.149 109 2.53 3.12
## Soil Winter 4.21 0.210 109 3.79 4.63
##
## Confidence level used: 0.95contrast(marginal,
method = "pairwise",
adjust = "tukey")## contrast estimate SE df t.ratio p.value
## Green tea Autumn - Rooibos Autumn -0.050 0.210 109 -0.240 1.0000
## Green tea Autumn - Soil Autumn -1.607 0.227 109 -7.080 <.0001
## Green tea Autumn - Green tea Spring 0.175 0.215 109 0.820 1.0000
## Green tea Autumn - Rooibos Spring -0.062 0.215 109 -0.290 1.0000
## Green tea Autumn - Soil Spring -1.647 0.258 109 -6.390 <.0001
## Green tea Autumn - Green tea Summer -0.065 0.221 109 -0.290 1.0000
## Green tea Autumn - Rooibos Summer -0.545 0.215 109 -2.530 0.3310
## Green tea Autumn - Soil Summer -1.787 0.227 109 -7.870 <.0001
## Green tea Autumn - Green tea Winter -0.014 0.210 109 -0.070 1.0000
## Green tea Autumn - Rooibos Winter -0.482 0.210 109 -2.290 0.4870
## Green tea Autumn - Soil Winter -1.872 0.258 109 -7.270 <.0001
## Rooibos Autumn - Soil Autumn -1.558 0.227 109 -6.860 <.0001
## Rooibos Autumn - Green tea Spring 0.225 0.215 109 1.050 0.9960
## Rooibos Autumn - Rooibos Spring -0.013 0.215 109 -0.060 1.0000
## Rooibos Autumn - Soil Spring -1.597 0.258 109 -6.200 <.0001
## Rooibos Autumn - Green tea Summer -0.015 0.221 109 -0.070 1.0000
## Rooibos Autumn - Rooibos Summer -0.495 0.215 109 -2.300 0.4810
## Rooibos Autumn - Soil Summer -1.738 0.227 109 -7.650 <.0001
## Rooibos Autumn - Green tea Winter 0.036 0.210 109 0.170 1.0000
## Rooibos Autumn - Rooibos Winter -0.433 0.210 109 -2.060 0.6530
## Rooibos Autumn - Soil Winter -1.822 0.258 109 -7.070 <.0001
## Soil Autumn - Green tea Spring 1.783 0.232 109 7.700 <.0001
## Soil Autumn - Rooibos Spring 1.545 0.232 109 6.670 <.0001
## Soil Autumn - Soil Spring -0.039 0.272 109 -0.140 1.0000
## Soil Autumn - Green tea Summer 1.543 0.237 109 6.520 <.0001
## Soil Autumn - Rooibos Summer 1.062 0.232 109 4.590 0.0010
## Soil Autumn - Soil Summer -0.180 0.243 109 -0.740 1.0000
## Soil Autumn - Green tea Winter 1.593 0.227 109 7.010 <.0001
## Soil Autumn - Rooibos Winter 1.125 0.227 109 4.950 <.0001
## Soil Autumn - Soil Winter -0.265 0.272 109 -0.980 0.9980
## Green tea Spring - Rooibos Spring -0.238 0.220 109 -1.080 0.9950
## Green tea Spring - Soil Spring -1.822 0.262 109 -6.970 <.0001
## Green tea Spring - Green tea Summer -0.240 0.225 109 -1.070 0.9950
## Green tea Spring - Rooibos Summer -0.720 0.220 109 -3.280 0.0590
## Green tea Spring - Soil Summer -1.963 0.232 109 -8.480 <.0001
## Green tea Spring - Green tea Winter -0.190 0.215 109 -0.880 0.9990
## Green tea Spring - Rooibos Winter -0.658 0.215 109 -3.060 0.1060
## Green tea Spring - Soil Winter -2.048 0.262 109 -7.830 <.0001
## Rooibos Spring - Soil Spring -1.584 0.262 109 -6.060 <.0001
## Rooibos Spring - Green tea Summer -0.002 0.225 109 -0.010 1.0000
## Rooibos Spring - Rooibos Summer -0.483 0.220 109 -2.200 0.5560
## Rooibos Spring - Soil Summer -1.725 0.232 109 -7.450 <.0001
## Rooibos Spring - Green tea Winter 0.048 0.215 109 0.220 1.0000
## Rooibos Spring - Rooibos Winter -0.420 0.215 109 -1.950 0.7230
## Rooibos Spring - Soil Winter -1.810 0.262 109 -6.920 <.0001
## Soil Spring - Green tea Summer 1.582 0.266 109 5.950 <.0001
## Soil Spring - Rooibos Summer 1.102 0.262 109 4.210 0.0030
## Soil Spring - Soil Summer -0.141 0.272 109 -0.520 1.0000
## Soil Spring - Green tea Winter 1.632 0.258 109 6.340 <.0001
## Soil Spring - Rooibos Winter 1.164 0.258 109 4.520 0.0010
## Soil Spring - Soil Winter -0.226 0.297 109 -0.760 1.0000
## Green tea Summer - Rooibos Summer -0.480 0.225 109 -2.130 0.6000
## Green tea Summer - Soil Summer -1.723 0.237 109 -7.280 <.0001
## Green tea Summer - Green tea Winter 0.050 0.221 109 0.230 1.0000
## Green tea Summer - Rooibos Winter -0.418 0.221 109 -1.890 0.7600
## Green tea Summer - Soil Winter -1.808 0.266 109 -6.790 <.0001
## Rooibos Summer - Soil Summer -1.242 0.232 109 -5.360 <.0001
## Rooibos Summer - Green tea Winter 0.531 0.215 109 2.470 0.3710
## Rooibos Summer - Rooibos Winter 0.062 0.215 109 0.290 1.0000
## Rooibos Summer - Soil Winter -1.327 0.262 109 -5.080 <.0001
## Soil Summer - Green tea Winter 1.773 0.227 109 7.800 <.0001
## Soil Summer - Rooibos Winter 1.305 0.227 109 5.740 <.0001
## Soil Summer - Soil Winter -0.085 0.272 109 -0.310 1.0000
## Green tea Winter - Rooibos Winter -0.468 0.210 109 -2.230 0.5350
## Green tea Winter - Soil Winter -1.858 0.258 109 -7.210 <.0001
## Rooibos Winter - Soil Winter -1.390 0.258 109 -5.390 <.0001
##
## P value adjustment: tukey method for comparing a family of 12 estimates(Shannon_pairwise <- cld(marginal,
alpha = 0.05,
Letters = letters,
adjust = "tukey")) # works with lm but not with two-factor ART## Sample.type Season emmean SE df lower.CL upper.CL .group
## Green tea Spring 2.16 0.155 109 1.71 2.62 a
## Green tea Autumn 2.34 0.149 109 1.90 2.77 a
## Green tea Winter 2.35 0.149 109 1.92 2.79 a
## Rooibos Autumn 2.39 0.149 109 1.95 2.82 a
## Rooibos Spring 2.40 0.155 109 1.95 2.85 a
## Green tea Summer 2.40 0.163 109 1.93 2.88 a
## Rooibos Winter 2.82 0.149 109 2.39 3.26 a
## Rooibos Summer 2.88 0.155 109 2.43 3.34 a
## Soil Autumn 3.95 0.172 109 3.44 4.45 b
## Soil Spring 3.99 0.210 109 3.37 4.60 b
## Soil Summer 4.13 0.172 109 3.62 4.63 b
## Soil Winter 4.21 0.210 109 3.60 4.82 b
##
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 12 estimates
## P value adjustment: tukey method for comparing a family of 12 estimates
## significance level used: alpha = 0.05(mod_S %>%
anova() %>%
mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
mod_S_ANOVA)## Analysis of Variance Table
##
## Response: Estimate
## Df Sum Sq Mean Sq F value Pr(>F) Part Eta Sq
## Sample.type 2 59.3 29.63 111.63 0.000 0.649
## Season 3 2.2 0.74 2.79 0.044 0.024
## Sample.type:Season 6 0.9 0.15 0.55 0.766 0.010
## Residuals 109 28.9 0.27 0.317# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_S, Sample.type ~ Season)
# summary(as.glht(pairs(marginal))) # fails because of unbalanced design
(mod_IS <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Inv. Simpson")))## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 7898 208 310 4261
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 6.25
## Estimated effects may be unbalanced
## [1] "Unequal group sizes - showing SS type III"
## Anova Table (Type III tests)
##
## Response: Estimate
## Sum Sq Df F value Pr(>F)
## (Intercept) 21664 1 554.18 <2e-16 ***
## Sample.type 7868 2 100.64 <2e-16 ***
## Season 281 3 2.40 0.072 .
## Sample.type:Season 310 6 1.32 0.253
## Residuals 4261 109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tables of means
## Grand mean
##
## 12.1
##
## Sample.type
## Green tea Rooibos Soil
## 5.94 9.1 26
## rep 45.00 46.0 30
##
## Season
## Autumn Spring Summer Winter
## 10.8 11 13.8 13
## rep 33.0 28 30.0 30
##
## Sample.type:Season
## Season
## Sample.type Autumn Spring Summer Winter
## Green tea 6.70 4.88 6.86 5.40
## rep 12.00 11.00 10.00 12.00
## Rooibos 8.25 8.08 10.67 9.45
## rep 12.00 11.00 11.00 12.00
## Soil 21.10 24.38 28.64 30.90
## rep 9.00 6.00 9.00 6.00## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 7898 208 310 4261
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 6.25
## Estimated effects may be unbalanced# Post-hoc test
marginal <- emmeans(mod_IS,
~ Sample.type : Season)
summary(marginal)## Sample.type Season emmean SE df lower.CL upper.CL
## Green tea Autumn 6.70 1.80 109 3.13 10.3
## Rooibos Autumn 8.25 1.80 109 4.68 11.8
## Soil Autumn 21.10 2.08 109 16.97 25.2
## Green tea Spring 4.88 1.89 109 1.14 8.6
## Rooibos Spring 8.08 1.89 109 4.35 11.8
## Soil Spring 24.38 2.55 109 19.32 29.4
## Green tea Summer 6.86 1.98 109 2.94 10.8
## Rooibos Summer 10.67 1.89 109 6.94 14.4
## Soil Summer 28.64 2.08 109 24.51 32.8
## Green tea Winter 5.40 1.80 109 1.82 9.0
## Rooibos Winter 9.45 1.80 109 5.87 13.0
## Soil Winter 30.90 2.55 109 25.84 36.0
##
## Confidence level used: 0.95contrast(marginal,
method = "pairwise",
adjust = "tukey")## contrast estimate SE df t.ratio p.value
## Green tea Autumn - Rooibos Autumn -1.55 2.55 109 -0.610 1.0000
## Green tea Autumn - Soil Autumn -14.40 2.76 109 -5.220 <.0001
## Green tea Autumn - Green tea Spring 1.82 2.61 109 0.700 1.0000
## Green tea Autumn - Rooibos Spring -1.38 2.61 109 -0.530 1.0000
## Green tea Autumn - Soil Spring -17.68 3.13 109 -5.650 <.0001
## Green tea Autumn - Green tea Summer -0.15 2.68 109 -0.060 1.0000
## Green tea Autumn - Rooibos Summer -3.97 2.61 109 -1.520 0.9320
## Green tea Autumn - Soil Summer -21.94 2.76 109 -7.960 <.0001
## Green tea Autumn - Green tea Winter 1.31 2.55 109 0.510 1.0000
## Green tea Autumn - Rooibos Winter -2.74 2.55 109 -1.070 0.9950
## Green tea Autumn - Soil Winter -24.19 3.13 109 -7.740 <.0001
## Rooibos Autumn - Soil Autumn -12.85 2.76 109 -4.660 0.0010
## Rooibos Autumn - Green tea Spring 3.37 2.61 109 1.290 0.9780
## Rooibos Autumn - Rooibos Spring 0.17 2.61 109 0.060 1.0000
## Rooibos Autumn - Soil Spring -16.13 3.13 109 -5.160 <.0001
## Rooibos Autumn - Green tea Summer 1.39 2.68 109 0.520 1.0000
## Rooibos Autumn - Rooibos Summer -2.42 2.61 109 -0.930 0.9990
## Rooibos Autumn - Soil Summer -20.39 2.76 109 -7.400 <.0001
## Rooibos Autumn - Green tea Winter 2.86 2.55 109 1.120 0.9930
## Rooibos Autumn - Rooibos Winter -1.19 2.55 109 -0.470 1.0000
## Rooibos Autumn - Soil Winter -22.65 3.13 109 -7.240 <.0001
## Soil Autumn - Green tea Spring 16.23 2.81 109 5.770 <.0001
## Soil Autumn - Rooibos Spring 13.02 2.81 109 4.630 0.0010
## Soil Autumn - Soil Spring -3.28 3.30 109 -0.990 0.9980
## Soil Autumn - Green tea Summer 14.25 2.87 109 4.960 <.0001
## Soil Autumn - Rooibos Summer 10.43 2.81 109 3.710 0.0160
## Soil Autumn - Soil Summer -7.54 2.95 109 -2.560 0.3170
## Soil Autumn - Green tea Winter 15.71 2.76 109 5.700 <.0001
## Soil Autumn - Rooibos Winter 11.66 2.76 109 4.230 0.0030
## Soil Autumn - Soil Winter -9.79 3.30 109 -2.970 0.1310
## Green tea Spring - Rooibos Spring -3.20 2.67 109 -1.200 0.9880
## Green tea Spring - Soil Spring -19.50 3.17 109 -6.150 <.0001
## Green tea Spring - Green tea Summer -1.98 2.73 109 -0.720 1.0000
## Green tea Spring - Rooibos Summer -5.79 2.67 109 -2.170 0.5730
## Green tea Spring - Soil Summer -23.76 2.81 109 -8.460 <.0001
## Green tea Spring - Green tea Winter -0.52 2.61 109 -0.200 1.0000
## Green tea Spring - Rooibos Winter -4.57 2.61 109 -1.750 0.8410
## Green tea Spring - Soil Winter -26.02 3.17 109 -8.200 <.0001
## Rooibos Spring - Soil Spring -16.30 3.17 109 -5.140 <.0001
## Rooibos Spring - Green tea Summer 1.23 2.73 109 0.450 1.0000
## Rooibos Spring - Rooibos Summer -2.59 2.67 109 -0.970 0.9980
## Rooibos Spring - Soil Summer -20.56 2.81 109 -7.320 <.0001
## Rooibos Spring - Green tea Winter 2.69 2.61 109 1.030 0.9970
## Rooibos Spring - Rooibos Winter -1.36 2.61 109 -0.520 1.0000
## Rooibos Spring - Soil Winter -22.81 3.17 109 -7.190 <.0001
## Soil Spring - Green tea Summer 17.52 3.23 109 5.430 <.0001
## Soil Spring - Rooibos Summer 13.71 3.17 109 4.320 0.0020
## Soil Spring - Soil Summer -4.26 3.30 109 -1.290 0.9780
## Soil Spring - Green tea Winter 18.98 3.13 109 6.070 <.0001
## Soil Spring - Rooibos Winter 14.93 3.13 109 4.780 <.0001
## Soil Spring - Soil Winter -6.52 3.61 109 -1.810 0.8110
## Green tea Summer - Rooibos Summer -3.81 2.73 109 -1.400 0.9620
## Green tea Summer - Soil Summer -21.78 2.87 109 -7.580 <.0001
## Green tea Summer - Green tea Winter 1.46 2.68 109 0.550 1.0000
## Green tea Summer - Rooibos Winter -2.59 2.68 109 -0.970 0.9980
## Green tea Summer - Soil Winter -24.04 3.23 109 -7.450 <.0001
## Rooibos Summer - Soil Summer -17.97 2.81 109 -6.390 <.0001
## Rooibos Summer - Green tea Winter 5.28 2.61 109 2.020 0.6780
## Rooibos Summer - Rooibos Winter 1.23 2.61 109 0.470 1.0000
## Rooibos Summer - Soil Winter -20.23 3.17 109 -6.370 <.0001
## Soil Summer - Green tea Winter 23.25 2.76 109 8.430 <.0001
## Soil Summer - Rooibos Winter 19.20 2.76 109 6.960 <.0001
## Soil Summer - Soil Winter -2.26 3.30 109 -0.680 1.0000
## Green tea Winter - Rooibos Winter -4.05 2.55 109 -1.590 0.9100
## Green tea Winter - Soil Winter -25.50 3.13 109 -8.160 <.0001
## Rooibos Winter - Soil Winter -21.45 3.13 109 -6.860 <.0001
##
## P value adjustment: tukey method for comparing a family of 12 estimates(InvSim_pairwise <- cld(marginal,
alpha = 0.05,
Letters = letters,
adjust = "tukey")) # works with lm but not with two-factor ART## Sample.type Season emmean SE df lower.CL upper.CL .group
## Green tea Spring 4.88 1.89 109 -0.62 10.4 a
## Green tea Winter 5.40 1.80 109 0.13 10.7 a
## Green tea Autumn 6.70 1.80 109 1.44 12.0 a
## Green tea Summer 6.86 1.98 109 1.09 12.6 a
## Rooibos Spring 8.08 1.89 109 2.58 13.6 a
## Rooibos Autumn 8.25 1.80 109 2.98 13.5 a
## Rooibos Winter 9.45 1.80 109 4.18 14.7 a
## Rooibos Summer 10.67 1.89 109 5.17 16.2 a
## Soil Autumn 21.10 2.08 109 15.02 27.2 b
## Soil Spring 24.38 2.55 109 16.93 31.8 b
## Soil Summer 28.64 2.08 109 22.56 34.7 b
## Soil Winter 30.90 2.55 109 23.45 38.3 b
##
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 12 estimates
## P value adjustment: tukey method for comparing a family of 12 estimates
## significance level used: alpha = 0.05(mod_IS %>%
anova() %>%
mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
mod_IS_ANOVA)## Analysis of Variance Table
##
## Response: Estimate
## Df Sum Sq Mean Sq F value Pr(>F) Part Eta Sq
## Sample.type 2 7898 3949 101.03 0.000 0.623
## Season 3 208 69 1.77 0.156 0.016
## Sample.type:Season 6 310 52 1.32 0.253 0.024
## Residuals 109 4261 39 0.336# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_IS, Sample.type ~ Season)
# summary(as.glht(pairs(marginal))) # fails because of unbalanced design
(mod_BP <- TestAlphaV3(filter(Richness_Diversity_long, Metric == "Berger Parker")))## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 0.854 0.132 0.128 2.172
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 0.141
## Estimated effects may be unbalanced
## [1] "Unequal group sizes - showing SS type III"
## Anova Table (Type III tests)
##
## Response: Estimate
## Sum Sq Df F value Pr(>F)
## (Intercept) 7.86 1 394.56 < 2e-16 ***
## Sample.type 0.82 2 20.46 2.9e-08 ***
## Season 0.12 3 1.99 0.12
## Sample.type:Season 0.13 6 1.07 0.38
## Residuals 2.17 109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tables of means
## Grand mean
##
## 0.28
##
## Sample.type
## Green tea Rooibos Soil
## 0.354 0.299 0.139
## rep 45.000 46.000 30.000
##
## Season
## Autumn Spring Summer Winter
## 0.304 0.299 0.223 0.293
## rep 33.000 28.000 30.000 30.000
##
## Sample.type:Season
## Season
## Sample.type Autumn Spring Summer Winter
## Green tea 0.33 0.37 0.27 0.42
## rep 12.00 11.00 10.00 12.00
## Rooibos 0.36 0.33 0.23 0.27
## rep 12.00 11.00 11.00 12.00
## Soil 0.17 0.14 0.12 0.13
## rep 9.00 6.00 9.00 6.00## Call:
## aov(formula = as.formula(paste(response_name, paste(factor_names[1],
## factor_names[2], sep = " * "), sep = " ~ ")), data = data2test)
##
## Terms:
## Sample.type Season Sample.type:Season Residuals
## Sum of Squares 0.854 0.132 0.128 2.172
## Deg. of Freedom 2 3 6 109
##
## Residual standard error: 0.141
## Estimated effects may be unbalanced# Post-hoc test
marginal <- emmeans(mod_BP,
~ Sample.type : Season)
summary(marginal)## Sample.type Season emmean SE df lower.CL upper.CL
## Green tea Autumn 0.334 0.0407 109 0.253 0.415
## Rooibos Autumn 0.365 0.0407 109 0.284 0.445
## Soil Autumn 0.165 0.0471 109 0.072 0.259
## Green tea Spring 0.373 0.0426 109 0.289 0.458
## Rooibos Spring 0.327 0.0426 109 0.243 0.412
## Soil Spring 0.141 0.0576 109 0.027 0.256
## Green tea Summer 0.274 0.0446 109 0.185 0.362
## Rooibos Summer 0.232 0.0426 109 0.148 0.317
## Soil Summer 0.120 0.0471 109 0.026 0.213
## Green tea Winter 0.422 0.0407 109 0.341 0.503
## Rooibos Winter 0.268 0.0407 109 0.188 0.349
## Soil Winter 0.128 0.0576 109 0.013 0.242
##
## Confidence level used: 0.95contrast(marginal,
method = "pairwise",
adjust = "tukey")## contrast estimate SE df t.ratio p.value
## Green tea Autumn - Rooibos Autumn -0.0308 0.0576 109 -0.530 1.0000
## Green tea Autumn - Soil Autumn 0.1685 0.0622 109 2.710 0.2370
## Green tea Autumn - Green tea Spring -0.0394 0.0589 109 -0.670 1.0000
## Green tea Autumn - Rooibos Spring 0.0066 0.0589 109 0.110 1.0000
## Green tea Autumn - Soil Spring 0.1925 0.0706 109 2.730 0.2270
## Green tea Autumn - Green tea Summer 0.0602 0.0604 109 1.000 0.9970
## Green tea Autumn - Rooibos Summer 0.1015 0.0589 109 1.720 0.8540
## Green tea Autumn - Soil Summer 0.2143 0.0622 109 3.440 0.0370
## Green tea Autumn - Green tea Winter -0.0881 0.0576 109 -1.530 0.9290
## Green tea Autumn - Rooibos Winter 0.0654 0.0576 109 1.140 0.9920
## Green tea Autumn - Soil Winter 0.2062 0.0706 109 2.920 0.1480
## Rooibos Autumn - Soil Autumn 0.1992 0.0622 109 3.200 0.0730
## Rooibos Autumn - Green tea Spring -0.0086 0.0589 109 -0.150 1.0000
## Rooibos Autumn - Rooibos Spring 0.0374 0.0589 109 0.630 1.0000
## Rooibos Autumn - Soil Spring 0.2233 0.0706 109 3.160 0.0810
## Rooibos Autumn - Green tea Summer 0.0910 0.0604 109 1.510 0.9360
## Rooibos Autumn - Rooibos Summer 0.1323 0.0589 109 2.240 0.5220
## Rooibos Autumn - Soil Summer 0.2451 0.0622 109 3.940 0.0080
## Rooibos Autumn - Green tea Winter -0.0573 0.0576 109 -1.000 0.9980
## Rooibos Autumn - Rooibos Winter 0.0962 0.0576 109 1.670 0.8780
## Rooibos Autumn - Soil Winter 0.2370 0.0706 109 3.360 0.0480
## Soil Autumn - Green tea Spring -0.2078 0.0634 109 -3.280 0.0600
## Soil Autumn - Rooibos Spring -0.1618 0.0634 109 -2.550 0.3210
## Soil Autumn - Soil Spring 0.0241 0.0744 109 0.320 1.0000
## Soil Autumn - Green tea Summer -0.1082 0.0649 109 -1.670 0.8780
## Soil Autumn - Rooibos Summer -0.0670 0.0634 109 -1.060 0.9960
## Soil Autumn - Soil Summer 0.0458 0.0665 109 0.690 1.0000
## Soil Autumn - Green tea Winter -0.2566 0.0622 109 -4.120 0.0040
## Soil Autumn - Rooibos Winter -0.1030 0.0622 109 -1.660 0.8840
## Soil Autumn - Soil Winter 0.0378 0.0744 109 0.510 1.0000
## Green tea Spring - Rooibos Spring 0.0460 0.0602 109 0.760 1.0000
## Green tea Spring - Soil Spring 0.2319 0.0716 109 3.240 0.0670
## Green tea Spring - Green tea Summer 0.0996 0.0617 109 1.620 0.9000
## Green tea Spring - Rooibos Summer 0.1409 0.0602 109 2.340 0.4550
## Green tea Spring - Soil Summer 0.2537 0.0634 109 4.000 0.0060
## Green tea Spring - Green tea Winter -0.0487 0.0589 109 -0.830 1.0000
## Green tea Spring - Rooibos Winter 0.1048 0.0589 109 1.780 0.8260
## Green tea Spring - Soil Winter 0.2456 0.0716 109 3.430 0.0390
## Rooibos Spring - Soil Spring 0.1859 0.0716 109 2.590 0.2960
## Rooibos Spring - Green tea Summer 0.0536 0.0617 109 0.870 0.9990
## Rooibos Spring - Rooibos Summer 0.0949 0.0602 109 1.580 0.9140
## Rooibos Spring - Soil Summer 0.2077 0.0634 109 3.270 0.0600
## Rooibos Spring - Green tea Winter -0.0947 0.0589 109 -1.610 0.9030
## Rooibos Spring - Rooibos Winter 0.0588 0.0589 109 1.000 0.9970
## Rooibos Spring - Soil Winter 0.1996 0.0716 109 2.790 0.2010
## Soil Spring - Green tea Summer -0.1323 0.0729 109 -1.810 0.8070
## Soil Spring - Rooibos Summer -0.0910 0.0716 109 -1.270 0.9810
## Soil Spring - Soil Summer 0.0218 0.0744 109 0.290 1.0000
## Soil Spring - Green tea Winter -0.2806 0.0706 109 -3.980 0.0070
## Soil Spring - Rooibos Winter -0.1271 0.0706 109 -1.800 0.8140
## Soil Spring - Soil Winter 0.0137 0.0815 109 0.170 1.0000
## Green tea Summer - Rooibos Summer 0.0412 0.0617 109 0.670 1.0000
## Green tea Summer - Soil Summer 0.1540 0.0649 109 2.380 0.4320
## Green tea Summer - Green tea Winter -0.1484 0.0604 109 -2.450 0.3800
## Green tea Summer - Rooibos Winter 0.0052 0.0604 109 0.090 1.0000
## Green tea Summer - Soil Winter 0.1460 0.0729 109 2.000 0.6900
## Rooibos Summer - Soil Summer 0.1128 0.0634 109 1.780 0.8260
## Rooibos Summer - Green tea Winter -0.1896 0.0589 109 -3.220 0.0700
## Rooibos Summer - Rooibos Winter -0.0361 0.0589 109 -0.610 1.0000
## Rooibos Summer - Soil Winter 0.1048 0.0716 109 1.460 0.9470
## Soil Summer - Green tea Winter -0.3024 0.0622 109 -4.860 <.0001
## Soil Summer - Rooibos Winter -0.1489 0.0622 109 -2.390 0.4210
## Soil Summer - Soil Winter -0.0080 0.0744 109 -0.110 1.0000
## Green tea Winter - Rooibos Winter 0.1535 0.0576 109 2.660 0.2580
## Green tea Winter - Soil Winter 0.2944 0.0706 109 4.170 0.0030
## Rooibos Winter - Soil Winter 0.1408 0.0706 109 2.000 0.6960
##
## P value adjustment: tukey method for comparing a family of 12 estimates(BP_pairwise <- cld(marginal,
alpha = 0.05,
Letters = letters,
adjust = "tukey")) # works with lm but not with two-factor ART## Sample.type Season emmean SE df lower.CL upper.CL .group
## Soil Summer 0.120 0.0471 109 -0.0178 0.257 a
## Soil Winter 0.128 0.0576 109 -0.0406 0.296 ab
## Soil Spring 0.141 0.0576 109 -0.0269 0.310 abc
## Soil Autumn 0.165 0.0471 109 0.0281 0.303 abc
## Rooibos Summer 0.232 0.0426 109 0.1081 0.357 abcd
## Rooibos Winter 0.268 0.0407 109 0.1495 0.387 abcd
## Green tea Summer 0.274 0.0446 109 0.1433 0.404 abcd
## Rooibos Spring 0.327 0.0426 109 0.2030 0.451 abcd
## Green tea Autumn 0.334 0.0407 109 0.2149 0.453 bcd
## Rooibos Autumn 0.365 0.0407 109 0.2457 0.484 cd
## Green tea Spring 0.373 0.0426 109 0.2490 0.497 cd
## Green tea Winter 0.422 0.0407 109 0.3030 0.541 d
##
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 12 estimates
## P value adjustment: tukey method for comparing a family of 12 estimates
## significance level used: alpha = 0.05(mod_BP %>%
anova() %>%
mutate(`Part Eta Sq`=`Sum Sq`/sum(`Sum Sq`) ) ->
mod_BP_ANOVA)## Analysis of Variance Table
##
## Response: Estimate
## Df Sum Sq Mean Sq F value Pr(>F) Part Eta Sq
## Sample.type 2 0.854 0.427 21.44 0.000 0.260
## Season 3 0.132 0.044 2.21 0.091 0.040
## Sample.type:Season 6 0.128 0.021 1.07 0.385 0.039
## Residuals 109 2.172 0.020 0.661# pwpp(marginal) # Pairwise P-value plot. Fails for unbalanced design
emmip(mod_BP, Sample.type ~ Season)
# summary(as.glht(pairs(marginal))) # fails because of unbalanced designPlot all alpha diversity metrics together
Richness_Diversity_long %>%
dplyr::filter(!Metric %in% c("Chao1", "ACE")) %>%
# mutate(across("Metric", ~fct_recode(., "Observed S" = "S obs.", "Inv. Simpson" = "Inv. Simpson", "Berger Parker" = "Berger Parker"))) %>%
mutate(across("Metric", ~fct_relevel(., "Observed S", "Inv. Simpson", "Shannon", "Berger Parker"))) %>%
mutate(across("Season", ~fct_relevel(., "Winter", "Spring", "Summer", "Autumn"))) %>%
droplevels() ->
Richness_Diversity_long2plot
p_alpha <- ggplot() +
geom_violin(data = Richness_Diversity_long2plot,
aes(
x = Sample.type,
y = Estimate,
ymin = lerr,
ymax = herr
), colour = "grey",
fill = "grey",
alpha = 1 / 3) +
geom_jitter(data = Richness_Diversity_long2plot,
aes(x = Sample.type,
y = Estimate,
ymin = lerr,
ymax = herr,
colour = Field), size = 3, width = 0.2, alpha = 2/3) +
# scale_colour_manual(values = Gradient.colours[c(5, 6, 11)], name = "") +
scale_colour_locuszoom(name = "") +
scale_fill_locuszoom(name = "") +
# geom_errorbar(alpha = 1 / 2, width = 0.3) +
xlab("") +
ylab("") +
theme(axis.text.x = element_text(
angle = 45,
vjust = 0.9,
hjust = 1
)) +
facet_grid(Metric ~ Season, scale = "free") +
theme(strip.text = element_text(size = f_size - 4)) +
background_grid(major = "y",
minor = "none",
size.major = 0.8)
bind_rows(`Observed S` = obsS_pairwise,
`Inv. Simpson` = InvSim_pairwise,
Shannon = Shannon_pairwise,
`Berger Parker` = BP_pairwise,
.id = "Metric") %>%
bind_cols(., y = rep(c(550, 60, 5.5, 1.1), each = 12)) %>%
mutate(across(Metric, ~fct_inorder(.x))) ->
dat_text
p_alpha <- p_alpha + geom_text(
data = dat_text,
mapping = aes(x = Sample.type, y = y, label = .group),
nudge_x = 0,
nudge_y = 0
)
print(p_alpha)
sessioninfo::session_info() %>%
details::details(
summary = 'Current session info',
open = TRUE
) Current session info
─ Session info ─────────────────────────────────────────────────────────────────────────
setting value
version R version 4.0.3 (2020-10-10)
os Ubuntu 18.04.5 LTS
system x86_64, linux-gnu
ui X11
language en_GB
collate en_DK.UTF-8
ctype en_DK.UTF-8
tz Europe/Prague
date 2021-05-21
─ Packages ─────────────────────────────────────────────────────────────────────────────
package * version date lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.0.2)
ade4 1.7-16 2020-10-28 [1] CRAN (R 4.0.2)
ape 5.5 2021-04-25 [1] CRAN (R 4.0.3)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
backports 1.2.1 2020-12-09 [1] CRAN (R 4.0.2)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.0.2)
Biobase 2.50.0 2020-10-27 [1] Bioconductor
BiocGenerics 0.36.1 2021-04-16 [1] Bioconductor
BiodiversityR * 2.13-1 2021-04-20 [1] CRAN (R 4.0.3)
biomformat 1.18.0 2020-10-27 [1] Bioconductor
Biostrings 2.58.0 2020-10-27 [1] Bioconductor
boot 1.3-28 2021-05-03 [1] CRAN (R 4.0.3)
broom * 0.7.6 2021-04-05 [1] CRAN (R 4.0.3)
bslib 0.2.5.1 2021-05-18 [1] CRAN (R 4.0.3)
car * 3.0-10 2020-09-29 [1] CRAN (R 4.0.2)
carData * 3.0-4 2020-05-22 [1] CRAN (R 4.0.2)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.2)
checkmate 2.0.0 2020-02-06 [1] CRAN (R 4.0.2)
class 7.3-19 2021-05-03 [1] CRAN (R 4.0.3)
cli 2.5.0 2021-04-26 [1] CRAN (R 4.0.3)
clipr 0.7.1 2020-10-08 [1] CRAN (R 4.0.2)
cluster 2.1.2 2021-04-17 [1] CRAN (R 4.0.3)
coda 0.19-4 2020-09-30 [1] CRAN (R 4.0.2)
codetools 0.2-18 2020-11-04 [1] CRAN (R 4.0.2)
coin 1.4-1 2021-02-08 [1] CRAN (R 4.0.3)
colorspace 2.0-1 2021-05-04 [1] CRAN (R 4.0.3)
cowplot * 1.1.1 2020-12-30 [1] CRAN (R 4.0.2)
crayon 1.4.1 2021-02-08 [1] CRAN (R 4.0.3)
curl 4.3.1 2021-04-30 [1] CRAN (R 4.0.3)
data.table 1.14.0 2021-02-21 [1] CRAN (R 4.0.3)
DBI 1.1.1 2021-01-15 [1] CRAN (R 4.0.3)
dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.0.3)
desc 1.3.0 2021-03-05 [1] CRAN (R 4.0.3)
DescTools 0.99.41 2021-04-10 [1] CRAN (R 4.0.3)
details 0.2.1 2020-01-12 [1] CRAN (R 4.0.2)
digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.2)
doParallel * 1.0.16 2020-10-16 [1] CRAN (R 4.0.2)
dplyr * 1.0.6 2021-05-05 [1] CRAN (R 4.0.3)
e1071 1.7-6 2021-03-18 [1] CRAN (R 4.0.3)
effects 4.2-0 2020-08-11 [1] CRAN (R 4.0.2)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.0.3)
emmeans * 1.6.0 2021-04-24 [1] CRAN (R 4.0.3)
estimability 1.3 2018-02-11 [1] CRAN (R 4.0.2)
evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.2)
Exact 2.1 2020-10-02 [1] CRAN (R 4.0.2)
expm 0.999-6 2021-01-13 [1] CRAN (R 4.0.3)
extrafont * 0.17 2014-12-08 [1] CRAN (R 4.0.2)
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fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.3)
farver 2.1.0 2021-02-28 [1] CRAN (R 4.0.3)
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foreach * 1.5.1 2020-10-15 [1] CRAN (R 4.0.2)
foreign 0.8-81 2020-12-22 [4] CRAN (R 4.0.3)
Formula 1.2-4 2020-10-16 [1] CRAN (R 4.0.2)
fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.2)
ggplot2 * 3.3.3 2020-12-30 [1] CRAN (R 4.0.2)
ggsci * 2.9 2018-05-14 [1] CRAN (R 4.0.2)
gld 2.6.2 2020-01-08 [1] CRAN (R 4.0.2)
glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
gridExtra * 2.3 2017-09-09 [1] CRAN (R 4.0.2)
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haven 2.4.1 2021-04-23 [1] CRAN (R 4.0.3)
highr 0.9 2021-04-16 [1] CRAN (R 4.0.3)
Hmisc 4.5-0 2021-02-28 [1] CRAN (R 4.0.3)
hms 1.1.0 2021-05-17 [1] CRAN (R 4.0.3)
htmlTable 2.2.1 2021-05-18 [1] CRAN (R 4.0.3)
htmltools 0.5.1.1 2021-01-22 [1] CRAN (R 4.0.3)
htmlwidgets 1.5.3 2020-12-10 [1] CRAN (R 4.0.2)
httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
igraph 1.2.6 2020-10-06 [1] CRAN (R 4.0.2)
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IRanges 2.24.1 2020-12-12 [1] Bioconductor
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jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.0.2)
kableExtra * 1.3.4 2021-02-20 [1] CRAN (R 4.0.3)
knitr * 1.33 2021-04-24 [1] CRAN (R 4.0.3)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.0.2)
lattice * 0.20-44 2021-05-02 [1] CRAN (R 4.0.3)
latticeExtra 0.6-29 2019-12-19 [1] CRAN (R 4.0.2)
libcoin 1.0-8 2021-02-08 [1] CRAN (R 4.0.3)
lifecycle 1.0.0 2021-02-15 [1] CRAN (R 4.0.3)
lme4 1.1-27 2021-05-15 [1] CRAN (R 4.0.3)
lmom 2.8 2019-03-12 [1] CRAN (R 4.0.2)
lmtest 0.9-38 2020-09-09 [1] CRAN (R 4.0.2)
lubridate 1.7.10 2021-02-26 [1] CRAN (R 4.0.3)
magrittr * 2.0.1 2020-11-17 [1] CRAN (R 4.0.2)
MASS * 7.3-54 2021-05-03 [1] CRAN (R 4.0.3)
Matrix 1.3-3 2021-05-04 [1] CRAN (R 4.0.3)
matrixStats 0.58.0 2021-01-29 [1] CRAN (R 4.0.3)
mgcv 1.8-35 2021-04-18 [1] CRAN (R 4.0.3)
minqa 1.2.4 2014-10-09 [1] CRAN (R 4.0.2)
mitools 2.4 2019-04-26 [1] CRAN (R 4.0.2)
modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.2)
modeltools 0.2-23 2020-03-05 [1] CRAN (R 4.0.2)
multcomp * 1.4-17 2021-04-29 [1] CRAN (R 4.0.3)
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openxlsx 4.2.3 2020-10-27 [1] CRAN (R 4.0.2)
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[4] /usr/lib/R/library